Latest Digital Development Outputs (China, Data, Economy/Platforms, Inclusion, Water, Rights, Sustainability) from CDD, Manchester

Recent outputs – on China Digital; Data-for-Development; Digital Economy / Platforms; Digital Inclusion; Digital Water; Rights; and Sustainability – from Centre for Digital Development researchers, University of Manchester:

CHINA DIGITAL

China’s digital expansion in the global South” presents recordings of nine presentations at a CDD international workshop that discusses the implications for the global South of China’s emergence as a digital superpower.

Understanding the evolution of China’s standardization policy system” (open access) by You-hong Yang, Ping Gao & Haimei Zhou, investigates the evolution of China’s technology standardization policy system in the period from 1978 to 2021.  

DATA-FOR-DEVELOPMENT

A DC State of Mind? A Review of the World Development Report 2021: Data for Better Lives by Hellen Mukiri-Smith, Laura Mann & Shamel Azmeh, reviews the World Development Report (2021) on data governance.

DIGITAL ECONOMY / PLATFORMS

Examining ecosystems and infrastructure perspectives of platforms: the case of small tourism service providers in Indonesia and Rwanda” (open access version available) by Christopher Foster & Caitlin Bentley, analyses tourism platforms from the perspective of small and marginal service providers. It is useful to move away from ideas of platform leaders organising ecosystems from the top-down, towards more emergent behaviours of service providers in multi-platform environments.

Automation and industrialisation through global value chains: North Africa in the German automotive wiring harness industry by Shamel Azmeh, Huong Nguyen & Marlene Kuhn, examines the implications of automation on the global map of production and the position of developing countries in global value chains. Through the case of the German automotive wiring harness industry, we examine the implications of ongoing automation processes on production in North Africa.

Digital public goods platforms for development: the challenge of scaling” (open access) by Brian Nicholson, Petter Nielsen, Sundeep Sahay & Johan Saebo.  We articulate the notion of digital global public goods and examine the development of DHIS2, a global health platform inspired by public goods, focusing on the paradoxes that arise in the scaling process. A presentation of the paper to the Pankhurst Institute, University of Manchester is available on YouTube.

DIGITAL INCLUSION

Digital inequality beyond the digital divide: conceptualizing adverse digital incorporation in the global South” (open access) by Richard Heeks, presents a new model to understand how inclusion in – rather than exclusion from – digital systems leads to inequality.

Revisiting digital inclusion: a survey of theory, measurement and recent research” (open access) by Matthew Sharp, sets out a framework of core components of digital inclusion, surveys current measures of digital inclusion, and makes suggestions for how future research could be more rigorous and useful.

DIGITAL WATER

Water ATMs and access to water: digitalisation of off-grid water infrastructure in peri-urban Ghana” (open access) by Godfred Amankwaa, Richard Heeks & Alison L. Browne, finds water ATMs to be incremental infrastructures delivering relatively limited and operational-level value, but also producing new and contested socio-material realities.

RIGHTS AND DIGITAL

RaFoLa: A Rationale-Annotated Corpus for Detecting Indicators of Forced Labour” (open access) by Erick Mendez Guzman, Viktor Schlegel & Riza Batista-Navarro, describes a dataset of news articles categorised according to forced labour indicators. The articles were annotated with rationales, i.e. human explanations for placing them under specific categories, to support the development of explainable AI systems.

Hustling day in Silicon Savannah: datafication and digital rights in East Africa” (open access) by Gianluca Iazzolino, Michael Kimani & Maddo, is a cartoon on the winners and losers in Kenya’s booming tech scene. It translates, for a non-academic audience, the authors’ research on how digital technologies are reshaping the informal economy in the global South.

SUSTAINABILITY AND DIGITAL

Exploring financing for green-tech SMEs in East Africa: current trends and risk appetite” (open access) by Aarti Krishnan, reviews the financing of green-tech SMEs in East Africa including different financing at different enterprise lifecycle stages, in different sectors, and across different countries.

Applications of Industry 4.0 digital technologies towards a construction circular economy: gap analysis and conceptual framework” by Faris Elghaish, Sandra T. Matarneh, David John Edwards, Farzad Pour Rahimian, Hatem El-Gohary & Obuks Ejohwomu, investigates the interrelationships between emerging digital technologies and the circular economy, concluding with the development of a conceptual digital ecosystem to integrate IoT, blockchain and AI.

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Income of Gig Work vs. Previous Job in Pakistan

Richard Heeks, Iftikhar Ahmad, Shanza Sohail, Sidra Nizamuddin, Athar Jameel, Seemab Haider Aziz, Zoya Waheed, Sehrish Irfan, Ayesha Kiran & Shabana Malik

Does the transition to gig work improve incomes in Pakistan?

Many workers join gig work platforms in the belief that their incomes will improve, but is this borne out in practice?  To investigate, the Centre for Labour Research interviewed 94 workers based on six platforms across three sectors: ride-hailing, food delivery, and personal care.

Of these, 51 were able to tell us what their previous monthly income had been in their most-recent employment prior to joining the platform[1].  Stated income varied from the equivalent of US$60 per month up to U$1,200 per month, and averaged US$220 per month[2].

After moving into gig work, average gross income was slightly higher, at US$240 per month but, as the graph below shows, there was a much more differentiated picture behind the average, with around 40% of respondents earning less gross income (red-bordered blue columns) than they had done previously.

However, as the graph also shows, things looked worse when comparing net income (orange columns).  For the great majority of prior jobs, work-related costs were small (only work-to-home transport, which we calculated based on typical commuting journeys in Pakistan to be just under US$18 per month; i.e. less than 10% of average gross income).  But for gig work – much of which relies on journeys by vehicle and continuous internet connectivity – the costs of petrol, maintenance and data eat heavily into gross income.  In addition, for some (only a few in our Pakistan sample) there are costs of renting their vehicle.[3]

These costs represented, on average, 65% of gross income and knocked average net income for gig workers down to just US$85 per month.  When we compare before-and-after for net income, then, we found more than 70% of our sample were earning less than in their previous job, and 45% earned over US$100 per month less.

This was especially an issue for ride-hailing drivers and it does reflect the particular circumstances during our interview period of late 2021 to early 2022: a drop-off in demand for travel due to Covid, and a steep rise in petrol prices.  Indeed, so bad was the problem that just over a fifth – 21 of the 94 – were reporting negative income.  That is, they were effectively paying to go to work as their costs exceeded their gross income; something to which the platforms responded in May 2022 by dropping the commission taken from drivers to 0%.

While recognising the challenging period for gig workers covered by our fieldwork, nonetheless, this does suggest that – by and large – gig work is not delivering the income boost that workers often hope for.  They may, for example, be lured by gross income figures, not realising how much lower net income will be.  Gig work does provide a livelihood – 40% of our sample were unemployed in the immediate period prior to joining – but it is not really fulfilling its promise.  It also falls far from decent work standards: five-sixths of those we interviewed took home less than a living wage.

If you’d like to know more, please refer to the 2022 Fairwork Report on Pakistan’s gig economy.


[1] Those who stated what their prior employment had been gave the following job descriptions: BPO operator, Teacher (2), Housekeeper, Shopkeeper, Gas company worker (2), Safety officer, Business person, Tanker driver, Ride-hailing driver with another platform (3), Traditional taxi driver (3), Farmer, Builder, Computer operator, Cook, Technician, Shop assistant, Domestic worker, Government worker

[2] This average is some way above the overall average earnings of US$140 per month but well below formal sector average monthly salary of US$480.

[3] For further detail, see this discussion of the breakdown of ride-hailing passenger payments.

Digital public goods platforms for development

Nicholson, B. Nielsen, P. Sahay, S. Saebo, J. Digital public goods platforms for development: The challenge of scaling The Information Society available open access at: https://www.tandfonline.com/doi/full/10.1080/01972243.2022.2105999

Recently there has been an explosion of research into digital platforms.  To provide an indication of the size of the output, a quick search on Google Scholar provided 3270000 “hits”, 39900 in 2022 alone to date with publications across diverse disciplines including management, information systems, economics and more.   In the realm of ICT4D, discourse has focused on how platforms may enable socio-economic development (Nicholson et al 2021) however there is a paucity of examples of empirical research on how this may be realised.  

Digital platforms are defined according to their principal purpose and identifies two broad categories: transaction platforms and innovation platforms. Transaction platforms refer to a two or multi sided marketplace mediated by the platform.  Innovation platforms act as “foundations upon which other firms can build complementary products, services or technologies” (Gawer, 2009, p. 54).

Most prior empirical research on digital platforms involves commercial, for-profit platforms situated in the regulative institutions of the Global North.  Inherent in this prior work is an assumption of “monetisation” and the capitalist market forces, and little is known about platforms that are donor supported and aimed at socio economic development.    

A forthcoming paper attempts to address the knowledge gap by conceptualising innovation platforms as public goods and asking:

How can innovation platforms be public goods?

A goal of the article is to identify the challenges of simultaneously scaling up digital platforms and developing them into public goods.  Empirically, the focus is on health, specifically the empirical example is the District Health Information System (DHIS2). 

The relevance of public goods in development is well-established in the domain of health.  Initiatives driven by global health organisations such as the World Bank and World Health Organization aim to promote digital public goods. Digital Square, a marketplace initiative in digital health, has developed a Global Goods Guidebook and a Global Goods Maturity Model.  Before and during the pandemic, open-source systems have been launched to support outbreak management, such as the Surveillance Outbreak Response Management and Analysis System (SORMAS). SORMAS intuitively displays features of a public good: it is free of charge, open source, independent from tech companies, and interoperable with other platforms such as DHIS2.

Turning to theory of public goods leads us to the economics discipline and centres on two main principles: non-rivalry and non-exclusion. “Goods” such as crime control, flood defences etc. are provided because of failure of the market mechanism.  Government thus intervenes either financially, through such mechanisms as taxation or licensing, or with direct provision.   Public goods are non-rivalrous, implying that one individual’s consumption of the good does not influence what is available for others. They are also non-excludable, in the sense that no one can be excluded from consumption of a public good. 

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Consider a lighthouse where one navigator’s use of the light does not prevent other navigators from doing the same. Many potential public goods exhibit only one of these properties resulting in the tragedy of the commons which can be illustrated with the example of a village pasture. Unrestricted access (non-exclusion) to the commons – pasture belonging to the village as a whole – leads to its degradation (rivalry). However, some scholars question the inevitability of depletion of common pool resources when they are managed in a bottom-up, cooperative way by those most dependant on them.  Under certain conditions, individuals govern themselves collectively, and without market pressures or government regulation, to obtain benefits, even if the temptation to freeride is present.

Global public goods are goods whose benefits cross borders and are global in scope for example eradication of infectious diseases where it is impossible to exclude any country from benefiting and each country will benefit without preventing another.

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The district health information system or DHIS2 supports decentralized routine health management. The architecture is designed with a generic core that enables local innovation and anyone with internet access can at any time download the most recent version of DHIS2, the source code, as well as required libraries and third-party products (such as Chrome or Firefox browsers). DHIS2 also comes with a set of bundled apps, developed by University of Oslo or through its partners in the Global South (such as HISP Tanzania, an independent entity with close collaboration with Oslo) available in an “app store.” It is similar in concept to Apple App Store or Google Play and some DHIS2 apps are also available on these platforms too. The platform architecture allows local innovation as apps, increasing its potential relevance globally.

Due to its openness and flexibility, it is impossible to know the exact number of DHIS2 implementations. It is known that ministries of health and other organizations in more than 100 developing countries use DHIS2, together covering an estimated population of 2.4 billion people.  In November 2020, the ministries of health in 73 countries (primarily developing countries) used DHIS2, out of which 60 were nationwide implementations, and 13 were in the pilot stage. In addition, 22 Indian states used DHIS2. There is also a range of other organizations using DHIS2 independently for reporting in the countries they are operating, including PEPFAR, Médecins Sans Frontières (MSF), International Medical Corps, Population Services International (PSI), and Save the Children.

We can explore the “qualification” of DHIS2 as a public good by considering some of the challenges experienced by developers in Oslo and other implementation sites examined as tensions and paradoxes.  In a seminal paper on paradoxes and theory building, Poole and van de Ven (1989) identify a paradox as “concerned with tensions and oppositions between well-founded, well-reasoned, and well-supported alternative explanations of the same phenomenon” (565). 

Consider the story of the product lead of the DHIS2 analytics team response to the challenge of prioritizing requests by developing a roadmap prioritization matrix. Most use-cases need analytics functionality, and a wide variety of requests are directed to this team. The product lead estimates that the analytics team can only accommodate about half the requests at any stage of the product development cycle. The question facing this individual is: “which requests should be prioritized, coming from whom, and in which release cycle?” The primary implementations of DHIS2 are users from governments in low- and middle-income countries, according to the product lead, who tend to not actively voice their requests for changes in functionality. These groups are constrained by physical separation often across great distance, limiting ability to meet in person and develop social relationships. By contrast, users from donor organizations and other users in the West, tend to have closer proximity and resources to visit Oslo and “make their voices heard,” resulting in greater influence over the DHIS2 functionality development. This mismatch led the product manager to develop this “objective” prioritization methodology.  From the perspective of public goods, the dynamics of donors’ activity affects the rivalry / excludability conditions as their greater influence means that other users are relatively excluded, and access is rivalrous depending on this influence.

There are also paradoxical consequences of scaling at the macro and micro levels.  While the Oslo development team add in their releases of new features for strengthening outputs and analysis towards a generic global platform, the typical user in a district of a developing country requires basic functionalities, and the new features often detract instead of increasing the software’s value for the users.  At the macro-level, the development team are seeking to cater to the universe of users, including district users, researchers, and data analytic experts in multiple country contexts. This requires them to continuously add new features, often for increasingly sophisticated use. This process went counter to the needs at the micro-level of the users in district offices, who want specific and easy to use functionalities for their everyday use.  Thinking again from a theoretical standpoint, the malleability of a digital good compared to the oft cited example of a static lighthouse is clearly evident. The drive towards generic global features at the macro level causes rivalry and excludes some users at the local more micro level.  

Overall, the more macro interests of the donors and drive towards a global generic platform appear incompatible with the smaller players who become increasingly marginalized. Furthermore, their capacity for collective action is limited by structural factors.   This challenges DHIS2’s status as a public good as we can see rivalry and exclusion creeping in.

The problem is not insurmountable, collective action and subsidiarity offer helpful mechanisms of governance. Two main subsidiarity conditions are known to be helpful related to effectiveness and necessity: that action should be taken at the level where it is most effective and that action at the higher level should be taken when lower levels cannot achieve the set goals by themselves. This is in line with ongoing efforts by Oslo to build South-South community-based networks and thereby decentralization into the Health Information System Programme (HISP) network. 

References

Gawer, A. (2009). Platform dynamics and strategies: from products to services. Platforms, markets and innovation45, 57.

Nicholson, B., Nielsen, P., & Sæbø, J. (2021). Digital platforms for development. Inf. Syst. J.31(6), 863-868.





Latest Digital Development Outputs (Data, Labour, Platforms, Society, Ed Tech, MSc) from CDD, Manchester

Recent outputs – on Data-for-Development; Digital Labour; Digital Platforms; Digital Society; Ed Tech; MSc Programme – from Centre for Digital Development researchers, University of Manchester:

DATA-FOR-DEVELOPMENT

Data Powered Positive Deviance: Combining Traditional and Non-Traditional Data to Identify and Characterise Development-Related Outperformers” (open access) by Basma Albanna, Richard Heeks, Julia Handl and colleagues from the DPPD project, presents a new methodology through which datasets can be used to identify “positive deviants” – those who outperform their peers in development – and to identify and scale the factors behind their outperformance.

Publication Outperformance among Global South Researchers: An Analysis of Individual-Level and Publication-Level Predictors of Positive Deviance” (open access) by Basma Albanna, Julia Handl & Richard Heeks, uses interviews, a survey and analysis of online datasets to identify those among a group of global South researchers who outperform their peers.  It identifies characteristics of both the high-performing researchers and their publications.

DIGITAL LABOUR

Systematic Evaluation of Gig Work Against Decent Work Standards: The Development and Application of the Fairwork Framework” (open access) by Richard Heeks, Mark Graham, Paul Mungai, Jean-Paul Van Belle & Jamie Woodcock, explains the development and application of the Fairwork framework, which is used worldwide to rate gig economy platforms against decent work standards.

Stripping Back the Mask: Working Conditions on Digital Labour Platforms during the COVID-19 Pandemic” (open access) by Kelle Howson, Funda Ustek-Spilda, Alessio Bertolini, Richard Heeks and other colleagues from the Fairwork project, analyses the Covid policies of 191 platforms in 43 countries. It finds some positive worker protections but also entrenchment of precarious work as platforms leverage the opportunities arising from the crisis.

DIGITAL PLATFORMS

Digital Platforms for Development” (open access) by Brian Nicholson, Petter Nielsen & Johan Saebo, provides an editorial introduction to a special issue of Information Systems Journal on the link between digital platforms and development processes.

Driving the Digital Value Network: Economic Geographies of Global Platform Capitalism” (open access) by Kelle Howson, Fabian Ferrari, Funda Ustek-Spilda, Richard Heeks and other colleagues from the Fairwork project, uses insights from global value chain and global production network frameworks to analyse power imbalances and value extraction across territories by gig economy platforms.

DIGITAL SOCIETY

“Toolkit for Measuring Digital Skills and Digital Literacy“ (open access) by authors at CSIS Indonesia, supported by Matthew Sharp, offers a comprehensive and original framework for measuring digital skills in Indonesia and other G20 countries. The toolkit incorporates insights from pilot individual and firm-level surveys on digital skills undertaken by CSIS in the Greater Jakarta area.

How can Smart City Shape a Happier Life? The Mechanism for Developing a Happiness Driven Smart City” by Huiying Zhu, Liyin Shen & Yitian Ren, introduces a Happiness Driven Smart City (HDSC) mechanism, composed of a three-layer structure and underpinned by a set of strategic measures. A case study shows the HDSC mechanism’s effectiveness in helping decision makers understand the status quo, strengths and weaknesses of smart city development in their context, so that their SC blueprint can be better aligned towards a happiness-driven direction.

ED TECH

The Effectiveness of Technology‐Supported Personalised Learning in Low‐and Middle‐Income Countries” (open access) by Louis Major, Gill Francis & Maria Tsapali, provides a meta-analysis examining the impact of students’ use of technology that personalises and adapts to learning level.

Evaluating Digital Personalised Learning Tools in Kenya: A New Research Study” (blog) by Becky Daltry, Louis Major and others, reports on a new research study to rigorously evaluate the integration of digital personalised learninginto Kenyan classrooms for young children, aged between 4-8 years old.

MSc PROGRAMME

Centre for Digital Development staff provide the core directorship and teaching for the University’s new MSc programme in Digital Development, which will launch in Sept 2022.

Distribution of Income from Motorcycle-Based Gig Work in Indonesia

When a consumer pays for motorcycle-based gig work, where does the money go?

Following the approach of an earlier, similar post on car ride-hailing,  and again using data gathered by the Fairwork Indonesia team in Jakarta, we can break this down using the generic model shown below:

a. Amount paid by customer: the service payment plus a platform fee (sometimes called an order or service or transaction processing fee) plus – sometimes – a tip.

b. Amount paid to platform: platforms typically take a commission (a set percentage of the customer service payment, usually between 10-25%) and often also charge a platform fee.

c. Amount paid to worker: all of the tip and the service payment minus the platform’s commission.  In some instances – at the end of a shift or at the end of a week – the worker might also get a bonus payment from the platform e.g. for completing a certain number of tasks or being available for work consistently and/or at particular times.  There may also be other criteria that impact access to bonus payments such as low order cancellation rates or high customer feedback ratings.  Bonuses are paid to the worker from the platform’s share which is taken from the platform’s commission; sometimes also from the platform fee; and in some instances more than this (in other words, in these cases, the worker earns more than the amount paid by the customer due to an additional subsidy taken by the platform from investment or other sources of capital).

The two charts below show the distribution of customer payments for two motorcycle-based gig work platforms (which were charging a 20% gross commission on the customer service payment plus a fee).  Figure 1 presents data for riders who own their own motorcycle (the majority of riders in our sample).  Figure 2 presents data for riders who finance their vehicle through loan repayments or (less frequently) rental.

We can draw a number of conclusions:

i. Shares of the Pie: the worker’s true net income (i.e. after work-related costs have been taken into account) is a significant share – around two-thirds – of the total payment made by the customer.  Aside from the net income earned by the worker, the great majority of the customer payment is captured by large private businesses; typically multinationals – the platform, fuel companies, vehicle finance houses, telecom providers.  A significant chunk of vehicle servicing and maintenance costs even goes this way via parts, oil, tyres, etc.

ii. Fuel Costs: fuel makes up a very significant proportion of costs: around 80% of costs for bike owners; about half of costs for those who finance their motorcycle.  It is therefore not surprising that the price of fuel is always at the forefront of workers’ minds: a relatively small rise can cause quite a significant reduction in their net income.

iii. Financing vs. Owning: as expected, the net income of those who finance their vehicle is a lower proportion of customer payment than that of vehicle owners.  In absolute terms, these two groups take home about the same net income (non-owners’ net income was about 5% lower).  It’s not completely clear how this happens but one contributing factor is that workers who finance their bikes work longer hours in order to help towards earning the extra to cover their repayments: an average 78-hour week compared to a 66-hour week for those who owned their bikes.

iv. Bonuses and Platform Subsidies: as noted below, the figures here are calculated on the basis of 23.5% of rider income deriving from platform bonus payments.  The platform gross commission plus fee represent just over 32% of the customer payment; yet the platform’s net earning is 5% or 6% only.  In other words, and absent unknown factors, the platform is on average paying substantially more than its entire commission to workers.

On this basis, one can calculate the tipping point at which platforms earn nothing and are having to subsidise worker income from investment or other sources of capital.  As illustrated in Figure 3, for this instance, this will happen when worker bonuses make up more than 30% of their income.  Yet one can find examples in Indonesia where the effect of bonuses is to more than double workers’ basic pay (i.e. bonuses make up more than 50% of worker income).  In such circumstances platforms must be significantly subsidising gig work from capital. If this is widespread, it may help to explain why so many gig work platforms report operating losses.

Network effects – the greater value of a platform to users as more users participate – would predict the emergence of monopoly (single seller of services to customers) and monopsony (single buyer of services from workers).  Yet this has not happened in most gig economy markets – including those of Indonesia – which, instead, are oligopolies/oligopsonies, meaning there is competition between platforms for both customers and workers.  It is that competition which in part motivates the payment of bonuses to workers.

Notes:

– Although insurance is shown as 0%, there are small payments against this item by some workers; just that they are so negligible a component that they rounded down to zero percent.

– The average figures we have included are that 25% of rider income is made up from tips and bonuses, of which tips make up 1.5%.  This must be seen as a very rough-and-ready average because platforms’ bonus payment schemes are continuously changing; their availability typically varies between workers (e.g. with tiered systems such that the highest bonus payments are only accessible by workers who meet particular criteria on workload, availability, cancellation rates, customer ratings, etc.); and workers’ ability to meet the targets necessary for bonus payment varies from day to day.  Bonuses are typically also only achievable for those working very long shifts: some of our sample were working 15- and in a couple of instances 18-hour days.

– The figures here do not take into account any customer-side promotions that platforms occasionally run; the assumption being that these may not alter the share of rider income.

– Fairwork data from South Africa showed riders’ net income to be 55% of the total customer payment, but this did not separately account for bonuses, which will increase the percentage.  Overall, distribution of income will vary between platforms and locations so the figures above should be seen as illustrative rather than universal.

Post by Richard Heeks, Treviliana Putri, Paska Darmawan, Amri Asmara, Nabiyla Risfa, Amelinda Kusumaningtyas & Ruth Simanjuntak.

Distribution of Income from Ride-Hailing in Indonesia

When a customer takes a taxi journey from a ride-hailing platform, where does the money go?

Using data gathered by the Fairwork Indonesia team in Jakarta, we can now break this down using the generic model shown below:

a. Amount paid by customer: the fare for the ride plus a platform fee (sometimes called an order or service or transaction processing fee) plus – sometimes – a tip.

b. Amount paid to platform: platforms typically take a commission (a set percentage of the customer fare, usually between 10-25%) and often also charge a platform fee.

c. Amount paid to worker: all of the tip and the fare minus the platform’s commission.  In some instances – at the end of a shift or at the end of a week – the worker might also get a bonus payment from the platform e.g. for completing a certain number of rides or being available for work consistently and/or at particular times of peak demand.  There may also be other criteria that impact access to bonus payments such as low order cancellation rates or high customer feedback ratings.  Bonuses are paid to the worker from the platform’s share which is taken from the platform’s commission; sometimes also from the platform fee; and in some instances more than this (in other words, in these cases, the worker earns more than the amount paid by the customer due to an additional subsidy taken by the platform from investment or other sources of capital).

The two charts below show the distribution of customer payments for two car ride-hailing platforms (which were charging a 20% gross commission on the customer fare plus a fee).  Figure 1 presents data for drivers who own their own vehicles (the minority of car taxi drivers in our sample).  Figure 2 presents data for drivers who finance their vehicle through loan repayments or (less frequently) rental.

We can draw a number of conclusions:

i. Worker Share of the Pie: the worker’s true net income (i.e. after work-related costs have been taken into account) is a minority share – around one-third – of the total payment made by the customer.

ii. Large Business Share of the Pie: aside from the net income earned by the worker, the great majority of the customer payment is captured by large private businesses; typically multinationals – the platform, fuel companies, vehicle finance houses, telecom providers.  A significant chunk of vehicle servicing and maintenance costs even goes this way via parts, oil, tyres, etc.

iii. Fuel Costs: fuel makes up a very significant proportion of costs: around 90% of costs for vehicle owners, who spend more on fuel than they earn in net terms; about half of costs for those who finance their vehicle.  It is therefore not surprising that the price of fuel is always at the forefront of workers’ minds: a relatively small rise can cause quite a significant reduction in their net income.

iv. Financing vs. Owning: not surprisingly, the net income of those who finance their vehicle is a lower proportion of customer payment than that of vehicle owners.  In absolute terms, these two groups take home about the same net income.  It’s not completely clear how this happens but one contributing factor is that workers who finance their vehicles work longer hours in order to help towards earning the extra to cover their repayments: an average 70-hour week compared to a 65-hour week for those who owned their cars.

Notes:

– Although insurance is shown as 0%, there are small payments against this item by some workers; just that they are so negligible a component that they rounded down to zero percent.

– The average figures we have included are that 15% of driver income is made up from tips and bonuses, of which tips make up 1.5% (i.e. one tenth of the extra).  This must be seen as a very rough-and-ready average because platforms’ bonus payment schemes are continuously changing; their availability typically varies between workers (e.g. with tiered systems such that the highest bonus payments are only accessible by workers who meet particular criteria on workload, availability, cancellation rates, customer ratings, etc.); and workers’ ability to meet the targets necessary for bonus payment varies from day to day.  Bonuses are typically also only achievable for those working very long shifts: some of our sample were working 15- and in a couple of instances 18-hour days.

– The figures here do not take into account any customer-side promotions that platforms occasionally run; the assumption being that these may not alter the share of driver income.

– Fairwork data from South Africa showed a similar financial distribution, with ride-hailing taxi drivers’ net income being 32% of the total customer payment.  However, distribution of income will vary between platforms and locations so the figures above should be seen as illustrative rather than universal.

Post by Richard Heeks, Treviliana Putri, Paska Darmawan, Amri Asmara, Nabiyla Risfa, Amelinda Kusumaningtyas & Ruth Simanjuntak.

Fairwork vis-à-vis ILO Decent Work Standards

Fairwork logoHow does the Fairwork framework of five decent work standards in the gig economy – fair pay, conditions, management, contracts, representation – compare to more conventional frameworks?

As explained in a recently-published paper, Fairwork is a simplified, revised and measurable version of the 11 elements of the International Labour Organization’s decent work agenda.

Comparing the two, as shown in the table above, Fairwork is not as comprehensive.  Some ILO elements not covered were seen as unrelated to Fairwork’s purpose.  For example, the contextual elements lie outside the control of platforms, and no evidence was found of child or forced labour.  Quantum of employment measures are not directly relevant to Fairwork’s aims though it would be informative to know if platforms are creating new work as opposed to just substituting for existing work.

While outside the scope of Fairwork’s principles, work security and flexibility were investigated via open questions in worker interviews.  Workers did raise the issues of flexibility and autonomy, as positive attributes of their gig economy work.  These supposed benefits are arguably more perceptual than real.  Hours of work are often determined by client demand and shaped by incentive payments offered by the platform to work at certain times or for certain shift lengths.  Work is recorded and managed via the app and platform to a significant extent.

In sum, the Fairwork framework covers the decent work-related issues identified in the research literature on platforms, and covers the majority of decent work elements within the ILO framework.  Its ratings could nonetheless be contextualised in a number of ways by adding in broader findings about national socio-economic context, about any creation of work and autonomy by the gig economy, about dimensions of inequality within and between gig sectors, and about longer-term job (in)security and precarity.

You can find more detail about this and other foundations for the Fairwork project in the open-access paper, “Systematic Evaluation of Gig Work Against Decent Work Standards: The Development and Application of the Fairwork Framework”; published in the journal, The Information Society.

Latest Digital Development Outputs (Data, Economy, Health, Platforms, Water) from CDD, Manchester

Using SmartphoneRecent outputs – on Data-for-Development; Digital Economy; Digital Health; Digital Platforms; Digital Water – from Centre for Digital Development researchers, University of Manchester:

DATA-FOR-DEVELOPMENT

Strengthening the Skills Pipeline for Statistical Capacity Development to Meet the Demands of Sustainable Development: Implementing a Data Fellowship Model in Colombia” (open access) by Pete Jones, Jackie Carter, Jaco Renken & Magdalena Arbeláez Tobón, considers the importance of quantitative data skills development implied by the UN Sustainable Development Goals. The success of a partnership programme in the UK is used to explore how ‘data fellowships’ can fulfil some of the unmet capacity needs of the SDGs in a developing country context, Colombia.

Building Information Modelling Diffusion Research in Developing Countries” (open access) by Samuel Adeniyi Adekunle, Obuks Ejohwomu & Clinton Ohis Aigbavboa undertakes a literature review – including current and future research trends – on the adoption of building information modelling in developing countries.

DIGITAL ECONOMY / PLATFORMS

Conceptualising Digital Platforms in Developing Countries as Socio-Technical Transitions” (open read access) by Juan Erasmo Gomez-Morantes, Richard Heeks & Richard Duncombe demonstrates how the multi-level perspective approach can be used to analyse the lifecycle of digital platforms: the process of innovation, rapidity of scaling, and development impacts relating to resource endowments, institutional formalisation, and shifts in power.

Digital Platforms and Institutional Voids in Developing Countries” (open access) by Richard Heeks, Juan Erasmo Gomez-Morantes, Brian Nicholson and colleagues from the Fairwork project, analyses how digital platforms change markets through their institutional actions.  Using the example of ride-hailing, it finds platforms have formed a market that is more efficient, effective, complete and formalised.  At the same time, though, they have institutionalised problematic behaviours and significant inequalities.

Navigating a New Digital Era Means Changing the World Economic Order” (open access) by Shamel Azmeh, discusses the implications of digital shifts for global economic governance.

DIGITAL HEALTH

Cost-Effectiveness of a Mobile Technology-Enabled Primary Care Intervention for Cardiovascular Disease Risk Management in Rural Indonesia” by Gindo Tampubolon and colleagues demonstrates how to determine the economic impact of m-health.  It calculates the cost-effectiveness of a mobile-based health intervention at c.US$4,300 per disability-adjusted life year averted and US$3,700 per cardiovascular disease event avoided.

Delivering Eye Health Education to Deprived Communities in India through a Social Media-Based Innovation” by Chandrani Maitra & Jenny Rowley aims to develop understanding of the benefits of, and the challenges associated with the use of social media to disseminate eye health information in deprived communities in India.

Using a Social Media Based Intervention to Enhance Eye Health Awareness of Members of a Deprived Community in India” (open access) by Chandrani Maitra & Jennifer Rowley reports on a WhatsApp-based intervention to promote eye health communication in deprived settings. This research highlights the potential benefits of WhatsApp in increasing awareness on eye problems, amongst deprived communities where the disease burden remains very high.

DIGITAL WATER

Digital Innovations and Water Services in Cities of the Global South: A Systematic Literature Review” (open access) by Godfred Amankwaa, Richard Heeks & Alison Browne reviews the literature on digital and water in Southern cities.  It summarises findings to date on implementation and impact and sets out the future research agenda.

A Better Way to Research Digital Platforms

Juan Paper Word CloudIn a new European Journal of Development Research paper – “Conceptualising Digital Platforms in Developing Countries as Socio-Technical Transitions” – I and my co-authors argue that there is a better way to research digital platforms.

Digital platforms play an ever-growing role within international development, and a body of research has emerged as a result.  This research offers valuable insights but we find three lacunae:

– Current work collectively identifies a whole set of factors at micro-, meso- and macro-levels that shape the trajectory of digital platforms.  But no research to date can encompass all of the factors and levels.

– Current work has been narrow and a-historical: it analyses the platform but not the existing ways of organising or delivering the particular social, economic or political activity that the platform competes with.

– Current work looks at either implementation and growth of platforms, or at their impact, but not both.  Yet implementation, scaling and impact of platforms are inextricably intertwined.

Our paper therefore uses a different and more holistic approach.  Understanding digital platforms as socio-technical transitions, it uses the multi-level perspective (MLP: see summary diagram below) as its analytical framework.

Using this framework, it analyses a successful ride-hailing platform – EasyTaxi in Colombia.  Although there were some challenges in applying the MLP framework, it addressed the three shortcomings of earlier work:

– It covers the broad range of factors that shape platforms at micro-, meso- and macro-level.

– By focusing on transition, it encompasses both the before and after of platform introduction.

– It analyses the platform lifecycle from initial innovation, though implementation and growth, to impact.

Thus, for example, the MLP explains how prior context and profile of traditional taxi driving created the landscape of infrastructure and incentives behind rapid scaling of the platform.  It also explains development impact: how resource endowments shifted between stakeholders; the formation and formalisation of institutional forces; and the changing distribution of power in the market.

On this basis, we recommend use of the multi-level perspective to researchers wanting to fully understand implementation and impact of digital platforms.

Digital Platforms as Institutions

platforms-as-institutionsHow should we understand digital platforms from an institutional perspective?

The paper, “Digital Platforms and Institutional Voids in Developing Countries”, suggests a four-layer model of institutional forms, and illustrates this using ride-hailing platforms as an example.

Layer 1: Digital Institutions.  Platforms themselves are institutions into which digitised routines and rules have been designed based on the digital affordances of the platform. Ride-hailing examples include algorithmic decision-making such as driver—customer matching, or price setting.

Layer 2: Digitally-Enabled Institutions.  Some institutional functions rely on digitised routines and rules within the platform but involve human intermediation.  Ride-hailing examples include checks on driver credentials for market entry, or adjudication of deactivation decisions.

Layer 3: Business Model Institutions.  These are broader rules and routines determined by the platform company as part of its business model, which govern participation in the platform but which exist outwith the digital platform.  Ride-hailing examples include control over vehicle entry into the market, determination of driver employment status, or setting the balance of supply and demand.

Layer 4: Stakeholder-Relation Institutions.  These are the connections or disconnections to other market or domain institutions.  Ride-hailing examples include relations to external stakeholders such as government agencies and trade unions.

Analysis of field evidence from Colombia and South Africa suggests that the first two types of institution are associated with the filling of prior institutional voids, and with market improvements.  The latter two institutional forms are more related to the maintenance, expansion or creation of institutional voids, and to market inequalities.

We look forward to further work applying and revising this institutional model of platforms.